This application relates to power management techniques.
Continuous increase in the electricity consumption around the world places considerable stress on aging power system infrastructures which have already been in use for many years. It is projected that electricity usage in the United States will increase from 3873 TWh in 2008 to 5021 TWh in 2035. Furthermore, summer peak demand in the U.S. is expected to increase by 40% from 2008 to 2030. Environmental pollution and global warming due to the use of fossil fuels for electricity generation and depletion of fossil fuel reserves have already raised serious concerns about sustainable operation of power systems in the future. Conventional power systems also suffer from low efficiency during generation (less than 40% thermal efficiency in steam turbines) and transmission of electricity over long distances from large power plants to load centers.
Ever increasing installation of Distributed Generations (DGs) and energy storage units in residential, commercial, and industrial buildings requires Demand Response (DR) programs which take into account customer side electric energy generation and storage capabilities. Currently there is no model available to advise an energy consumer on whether at each instant of time its demand can be met by the sum of DG output powers and the storage discharge power without decreasing the storage life-time or extra power from the utility (grid) should be purchased.
Conventional DR programs in the distribution level only focus on the interaction between the utility and the end-users based on the grid price signal or incentives offered by the utility. Previous studies in this area neglected the effects of having distributed generations and energy storage units on the energy consumer decision to participate in a demand response program.
A microgrid needs to have a real-time power management system to balance its electricity supply and demand during both transient and steady-state periods. Electric loads can vary significantly based on time of the day, season and load type (residential, commercial, industrial, etc.). Renewable distributed generation outputs also change continuously depending on the irradiation level, wind speed and other meteorological parameters. The pattern of change in DG power outputs can be totally independent of the changes in the load. Forecasting tools have been widely developed and used to estimate the future generation and demand profiles.
Forecasted data can be used to schedule generation from local dispatchable sources (if any) or importing a certain level of power from the grid to a microgrid on a long time-frame basis (e.g. hourly) to support the shortage in renewable generation. However, forecasting errors and fast variations (e.g. minute by minute) in the load and DG power outputs always introduce uncertainty to a microgrid operation which can only be addressed by implementing a real-time power management system.
Batteries can act as a buffer to alleviate the mismatch of generation and demand in a microgrid. In this way, when DGs output power is more than the demand, battery is charged. The battery is discharged during times of low generation and high demand to reduce the power mismatch.
Due to rapid changes in the power output of renewable energy sources over time and variations in the demand, a battery might experience a very irregular pattern of charge and discharge in a microgrid if not controlled properly. This will have a negative impact on battery lifetime and will increase the overall operational cost of the microgrid. Therefore, in addition to balancing supply and demand in real-time, power management system should operate the battery in a way to minimize operational cost of a microgrid.
Typically, the cost components of a battery over a certain time horizon (typically based on useful lifetime of the overall project) including the battery capital cost, replacement cost, as well as operation and maintenance cost (O&MC) are converted into equivalent uniform annual cost (EUAC) as follows:
                    EUAC        =                                                            RBC                ×                                  {                                                            [                                              CC                        +                                                  RC                          ×                                                      SFF                            ⁡                                                          (                                                                                                i                                  act                                                                ,                                                                  Y                                  rep                                                                                            )                                                                                                                          ]                                        ×                                          CRF                      ⁡                                              (                                                                              i                            act                                                    ,                                                      Y                            proj                                                                          )                                                                              }                                            +                              RBC                ×                O                                      &                    ⁢          MC          ×                                    (                              1                +                f                            )                        n                                              (        1        )                                                          ⁢        where                                                                                      ⁢                              SFF            ⁢                          (                                                i                  act                                ,                                  Y                  rep                                            )                                =                                    ∑                              i                =                1                                            NO                ,                rep                                      ⁢                                                  ⁢                          1                                                (                                      1                    +                                          i                      act                                                        )                                                  n                  ×                                      Y                    rep                                                                                                          (        2        )                                                          ⁢                              CRF            ⁡                          (                                                i                  act                                ,                                  Y                  proj                                            )                                =                                    (                                                i                  act                                ×                                                      (                                          1                      +                                              i                        act                                                              )                                                        Y                    proj                                                              )                        /                          (                                                                    (                                          1                      +                                              i                        act                                                              )                                                        Y                    proj                                                  -                1                            )                                                          (        3        )                                                          ⁢                              i            act                    =                                    (                                                i                  nom                                -                f                            )                        /                          (                              1                +                f                            )                                                          (        4        )            Once EUAC is determined, the storage cost can be obtained by dividing EUAC by the expected annual kWh usage of the battery. This approach considers a longer time horizon for battery cost calculation compared to the first method. However, to make a dispatching decision, the future battery usage pattern in a year cannot be assumed a priory.
Both the above cost calculation methods lack a modeling tool to include the effect of DOD and discharge current for individual discharge events on the battery lifetime and the battery cost.
Another solution is the HOMER energy modeling software from NREL. HOMER is used as a baseline to compare the results of proposed intelligent management system with. HOMER user can select between several dispatch strategies and compare the results. However, all these strategies use a wear cost model for batteries which is based on EFC concept. In this model, first, cycle life versus DOD data points of a battery (which should be provided by the user or selected from the program's database) are used to calculate battery lifetime throughput at different DODs as follows:Qlifetime,i=fi×DODi×Vbattery×CR/1000  (5)HOMER then averages all lifetime throughputs (within the acceptable range of DOD) obtained from (5) which results in a single constant lifetime throughput value for the battery (Qlifetime). This value is considered to be the overall kWh energy that can be fed into a battery over its lifetime. Battery wear cost is then calculated by dividing replacement cost of a battery by its lifetime throughput, as follows:Cbw=(RBC×RC)/(Qlifetime×μrt)  (6)Finally, CEB in HOMER is calculated by adding up the battery wear cost and cost of charging the battery:CEBHOMER=Cbw+Ccharge  (7)CEB in HOMER is used to compare marginal cost of generation from a battery (discharging) with other available sources of energy to apply economic dispatch in a microgrid.
HOMER battery cost model is simple and needs minimal data about battery characteristics, however there are major drawbacks compared to the cost model which is used in intelligent power and load management system (IPLMS) proposed in this work. These drawbacks can be summarized as follows:                1) In HOMER only the effect of DOD on battery lifetime can be modeled while IPLMS cost model considers both DOD and discharge rate as parameters affecting a battery lifetime.        2) HOMER model is based on averaging all data points to obtain a single value for the battery lifetime throughput. This assumes that the battery has an equal exposure to all levels of DOD (within the acceptable range) over its lifetime. In practice, however, a battery might experience high or low levels of DOD more often depending on the application and system specifics. In IPLMS, for each individual discharge event, DOD and its effect on the battery lifetime are calculated separately. This results in a more realistic life estimation and consequently cost model for a battery.        